General Setup

General Setup


Create a new analysis directories.

- general directory

- stains directory

- for plots

- for output of summary results

- for baseline tables

- for genetic analyses

- for Cox regression results
source("scripts/functions.R")
source("scripts/pack02.packages.R")

* General packages...

* Genomic packages...
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
source("scripts/colors.R")

Background

This notebook contains additional figures of the project.

Loading data

load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
# load(paste0(PROJECT_loc, "/20241017.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))

Fix some variables

We need to get the ‘conventional unit’ versions of cholesterols.

AERNASE.clin <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

Additional figures

Age and sex

We want to create per-age-group figures median ± interquartile range.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by sex.
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).

# ?ggpubr::ggboxplot()
compare_means(HMOX1 ~ Gender,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("Gender"),
                  y = "HMOX1", 
                  xlab = "gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Gender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

library(dplyr)

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
       
        female male
  <55       16   38
  55-64     82  195
  65-74    114  318
  75-84     84  213
  85+       12   20
table(AERNASE.clin$AgeGroupSex)

   <55 females      <55 males 55-64 females     55-64 males  65-74 females    65-74 males  75-84 females    75-84 males    85+ females      85+ males 
            16             38             82            195            114            318             84            213             12             20 

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and age group as median ± interquartile range.


# ?ggpubr::ggboxplot()
compare_means(HMOX1 ~ AgeGroup,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "HMOX1", 
                  xlab = "Age groups (years)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AgeGroup.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "HMOX1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "HMOX1 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Hypertension & blood pressure

We want to create figures of CONVOCALS_downstream levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by hypertension group (no, yes)
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin$SBPGroup, AERNASE.clin$Gender)
         
          female male
  <120        18   57
  120-139     63  138
  140-159     79  211
  160+       116  260

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and hypertension/blood pressure group as median ± interquartile range.

HMOX1

detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)
compare_means(HMOX1 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
filter: removed 150 rows (14%), 942 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
filter: removed 150 rows (14%), 942 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HMOX1", 
                  xlab = "Hypertension medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.HypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
filter: removed 150 rows (14%), 942 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 150 rows (14%), 942 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HMOX1", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed 151 rows (14%), 941 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed 151 rows (14%), 941 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed 31 rows (3%), 1,061 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed 31 rows (3%), 1,061 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Hypercholesterolemia & LDL levels

We want to create figures of CONVOCALS_downstream levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by hypercholesterolemia (risk614) group (no, yes)
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by lipid-lowering drugs group (no, yes)
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin$LDLGroup, AERNASE.clin$Gender)
         
          female male
  <100        83  219
  100-129     49  120
  130-159     33   68
  160-189     16   24
  190+         9   14
require(sjlabelled)

AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)

table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)
   
     no yes
  0 306   0
  1   0 697
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

HMOX1


compare_means(HMOX1 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
filter: removed 457 rows (42%), 635 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 457 rows (42%), 635 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
filter: removed 457 rows (42%), 635 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 457 rows (42%), 635 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
filter: removed 89 rows (8%), 1,003 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 89 rows (8%), 1,003 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
filter: removed 89 rows (8%), 1,003 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 89 rows (8%), 1,003 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HMOX1", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Med.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HMOX1", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 1,091 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed 458 rows (42%), 634 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
filter: removed 458 rows (42%), 634 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed 90 rows (8%), 1,002 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
filter: removed 90 rows (8%), 1,002 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Kidney function (eGFR)

We want to create figures of CONVOCALS_downstream levels stratified by kidney function.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin$eGFRGroup, AERNASE.clin$Gender)
       
        female male
  <15        1    0
  15-29      3   11
  30-59     77  165
  60-89    165  409
  90+       47  155
table(AERNASE.clin$eGFRGroup, AERNASE.clin$KDOQI)
       
        No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
  <15                           0                      0            0                0              0               1
  15-29                         0                      0            0                0             14               0
  30-59                         0                      0            0              242              0               0
  60-89                         0                      0          574                0              0               0
  90+                           0                    202            0                0              0               0

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and kidney function group as median ± interquartile range.

HMOX1


# Global test

compare_means(HMOX1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
filter: removed 28 rows (3%), 1,064 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HMOX1", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
filter: removed 28 rows (3%), 1,064 rows remaining
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.KDOQI.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
filter: removed 28 rows (3%), 1,064 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HMOX1", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 28 rows (3%), 1,064 rows remaining
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.KDOQI_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ eGFRGroup,  data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "HMOX1 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 59 rows (5%), 1,033 rows remaining
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

BMI

We want to create figures of CONVOCALS_downstream levels stratified by BMI.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by BMI WHO group (underweight, normal, overweight, obese)
  • Box and Whisker plot for CONVOCALS_downstream plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)

table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
         
          female male
  <18.5        6    4
  18.5-24    118  273
  25-29      120  373
  30-35       37   89
  35+         13   17
table(AERNASE.clin$BMIGroup, AERNASE.clin$BMI_WHO)
         
          No data available/missing Underweight Normal Overweight Obese
  <18.5                           0           9      0          0     0
  18.5-24                         0           0    391          0     0
  25-29                           0           0      0        492     0
  30-35                           0           0      0          0   126
  35+                             0           0      0          0    30

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and age group as median ± interquartile range.

HMOX1


# Global test
compare_means(HMOX1 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
filter: removed 42 rows (4%), 1,050 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 42 rows (4%), 1,050 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
filter: removed 42 rows (4%), 1,050 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 42 rows (4%), 1,050 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
filter: removed 44 rows (4%), 1,048 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "HMOX1 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 44 rows (4%), 1,048 rows remaining
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI_byWHO.pdf"), plot = last_plot())
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Diabetes

We want to create figures of CONVOCALS_downstream levels stratified by type 2 diabetes.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and age group as median ± interquartile range.

HMOX1


compare_means(HMOX1 ~ DiabetesStatus,  
              data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
filter: no rows removed
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "HMOX1",
                  xlab = "Diabetes status",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: no rows removed
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Diabetes.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
filter: no rows removed
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "HMOX1",
                  xlab = "Diabetes status per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: no rows removed
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Diabetes_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

NA
NA

Smoking

We want to create figures of CONVOCALS_downstream levels stratified by smoking.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and age group as median ± interquartile range.

HMOX1


# Global test
compare_means(HMOX1 ~ SmokerStatus,  data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
filter: removed 45 rows (4%), 1,047 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HMOX1", 
                  xlab = "Smoker status",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 45 rows (4%), 1,047 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Smoking.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
filter: removed 45 rows (4%), 1,047 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HMOX1", 
                  xlab = "Smoker status per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 45 rows (4%), 1,047 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Smoking_byGender.pdf"), plot = last_plot())
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Stenosis

We want to create figures of CONVOCALS_downstream levels stratified by stenosis grade.

  • Box and Whisker plot for CONVOCALS_downstream plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin$StenoticGroup, AERNASE.clin$Gender)
       
        female male
  <70       17   61
  70-89    151  358
  90+      133  342
table(AERNASE.clin$stenose, AERNASE.clin$StenoticGroup)
                  
                   <70 70-89 90+
  missing            0     0   0
  0-49%              5     0   0
  50-70%            73     0   0
  70-90%             0   509   0
  90-99%             0     0 438
  100% (Occlusion)   0     0  10
  NA                 0     0   0
  50-99%             0     0   4
  70-99%             0     0  23
  99                 0     0   0

Now we can draw some graphs of plaque CONVOCALS_downstream levels per sex and age group as median ± interquartile range.

HMOX1


# Global test
compare_means(HMOX1 ~ StenoticGroup,  data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HMOX1", 
                  xlab = "Stenotic grade",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Stenosis.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(HMOX1 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HMOX1", 
                  xlab = "Stenotic grade per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 30 rows (3%), 1,062 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Stenosis_byGender.pdf"), plot = last_plot())
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Symptoms

We want to create per-symptom figures.

library(dplyr)

table(AERNASE.clin$AgeGroup, AERNASE.clin$AsymptSympt2G)
       
        Asymptomatic Symptomatic
  <55             10          44
  55-64           32         245
  65-74           47         385
  75-84           20         277
  85+              2          30
table(AERNASE.clin$Gender, AERNASE.clin$AsymptSympt2G)
        
         Asymptomatic Symptomatic
  female           24         284
  male             87         697
table(AERNASE.clin$AsymptSympt2G)

Asymptomatic  Symptomatic 
         111          981 

Now we can draw some graphs of plaque CONVOCALS_downstream levels per symptom group as median ± interquartile range.

HMOX1


# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "HMOX1",
                  title = "HMOX1 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HMOX1 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AsymptSympt2G.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(p1)

compare_means(HMOX1 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "HMOX1",
                  title = "HMOX1 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(p1)

Forest plots

We would also like to visualize the multivariable analyses results.

library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target
NA

Forest plots.

HMOX1

dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="HMOX1"]),
                  low = c(models_target$low95CI[models_target$Predictor=="HMOX1"]),
                  high = c(models_target$up95CI[models_target$Predictor=="HMOX1"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque HMOX1 normalized expression (1 SD increment, n = 622)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.forest.pdf"), plot = fp)
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rm(fp)

Target expression vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the CONVOCALS_downstream plaque levels. These include:

  • IL2
  • IL4
  • IL5
  • IL6
  • IL8
  • IL9
  • IL10
  • IL12
  • IL13
  • IL21
  • INFG
  • TNFA
  • MIF
  • MCP1
  • MIP1a
  • RANTES
  • MIG
  • IP10
  • Eotaxin1
  • TARC
  • PARC
  • MDC
  • OPG
  • sICAM1
  • VEGFA
  • TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay.

  • MMP2
  • MMP8
  • MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the CONVOCALS_downstream` plaque levels.

We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.

Prepare data

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"
names(AERNASE.clin)[names(AERNASE.clin) == "MMP9"] <- "MMP9rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin <- merge(AERNASE.clin, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))Warning: `mutate_each()` was deprecated in dplyr 0.7.0.
Please use `across()` instead.
Error in `mutate_each_impl()`:
! Can't select columns that don't exist.
✖ Columns `IL2`, `IL4`, `IL5`, `IL8`, `IL9`, etc. don't exist.
Backtrace:
 1. AERNASE.clin %>% ...
 2. dplyr::mutate_each(., funs(as.numeric), proteins_of_interest)
 3. dplyr:::mutate_each_impl(tbl, funs, enquos(...), "mutate_each")

Visualize transformations

We will just visualize these transformations.

Correlations

Here we calculate correlations between CONVOCALS_downstream and 28 other cytokines. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:

  • L: LUMINEX
  • E: ELISA
  • a: activity assay

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of CONVOCALS_downstream` with other cytokines only.

HMOX1

Another version - probably not good.

Target expression vs. cytokines plaque levels lm()

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque CONVOCALS_downstream levels.

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque CONVOCALS_downstream levels.

DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Target expression vs. vulnerability index

Here we plot the levels of inverse-rank normal transformed CONVOCALS_downstream plaque levels from experiment 1 and 2 to the Plaque vulnerability index.

Visualisations

HMOX1

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability index as a function of plaque CONVOCALS_downstream levels.

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..

Session information


Version:      v1.1.1
Last update:  2024-01-09
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse Targets from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_

_W_

**Changes log**
* v1.1.1 Textual fixes.
* v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
* v1.0.1 Fix to the start of this notebook.
* v1.0.0 Inital version.

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
© 1979-2024 Sander W. van der Laan | s.w.vanderlaan[at]gmail[dot]com | vanderlaanand.science.
---
title: "Additional Figures"
subtitle: Accompanying 'CONVOCALS_downstream'
author: '[Sander W. van der Laan, PhD](https://vanderlaanand.science) | s.w.vanderlaan[at]gmail[dot]com'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack02.packages.R")
```

```{r Setting: Colors}
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
source("scripts/colors.R")
```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")
```


# Background

This notebook contains additional figures of the project.


# Loading data

```{r Loading project data}
load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
# load(paste0(PROJECT_loc, "/20241017.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```


# Fix some variables

We need to get the 'conventional unit' versions of cholesterols.

```{r}
AERNASE.clin <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```


# Additional figures

## Age and sex
We want to create per-age-group figures median ± interquartile range. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by sex.
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


```{r per Sex}

# ?ggpubr::ggboxplot()
compare_means(HMOX1 ~ Gender,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("Gender"),
                  y = "HMOX1", 
                  xlab = "gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Gender.pdf"), plot = last_plot())


```

```{r AgeGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
table(AERNASE.clin$AgeGroupSex)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

```{r per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(HMOX1 ~ AgeGroup,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "HMOX1", 
                  xlab = "Age groups (years)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(HMOX1 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "HMOX1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "HMOX1 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())




```


## Hypertension & blood pressure
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypertension group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin$SBPGroup, AERNASE.clin$Gender)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypertension/blood pressure group as median ± interquartile range.

### HMOX1

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```


```{r }
compare_means(HMOX1 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HMOX1", 
                  xlab = "Hypertension medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(HMOX1 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HMOX1", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(HMOX1 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "HMOX1", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "HMOX1", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin$LDLGroup, AERNASE.clin$Gender)

```


```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)

AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)

table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

### HMOX1

```{r }

compare_means(HMOX1 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(HMOX1 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(HMOX1 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(HMOX1 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HMOX1", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HMOX1", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(HMOX1 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "HMOX1", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "HMOX1", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "HMOX1 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```


## Kidney function (eGFR)
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by kidney function. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin$eGFRGroup, AERNASE.clin$Gender)

table(AERNASE.clin$eGFRGroup, AERNASE.clin$KDOQI)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and kidney function group as median ± interquartile range.

### HMOX1

```{r}

# Global test

compare_means(HMOX1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR.pdf"), plot = last_plot())

compare_means(HMOX1 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(HMOX1 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HMOX1", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "HMOX1 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.KDOQI.pdf"), plot = last_plot())

compare_means(HMOX1 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HMOX1", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(HMOX1 ~ eGFRGroup,  data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "HMOX1", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "HMOX1 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```


## BMI
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by BMI. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)

table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
table(AERNASE.clin$BMIGroup, AERNASE.clin$BMI_WHO)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### HMOX1

```{r}

# Global test
compare_means(HMOX1 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI.pdf"), plot = last_plot())

compare_means(HMOX1 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(HMOX1 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "HMOX1", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "HMOX1 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by type 2 diabetes. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### HMOX1

```{r per Diabetes per Sex}

compare_means(HMOX1 ~ DiabetesStatus,  
              data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "HMOX1",
                  xlab = "Diabetes status",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Diabetes.pdf"), plot = last_plot())


compare_means(HMOX1 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "HMOX1",
                  xlab = "Diabetes status per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Diabetes_byGender.pdf"), plot = last_plot())
  

```


## Smoking
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by smoking. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### HMOX1 

```{r per Smoking per Sex}

# Global test
compare_means(HMOX1 ~ SmokerStatus,  data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HMOX1", 
                  xlab = "Smoker status",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Smoking.pdf"), plot = last_plot())

compare_means(HMOX1 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HMOX1", 
                  xlab = "Smoker status per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Smoking_byGender.pdf"), plot = last_plot())

```


## Stenosis
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by stenosis grade. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin$StenoticGroup, AERNASE.clin$Gender)
table(AERNASE.clin$stenose, AERNASE.clin$StenoticGroup)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### HMOX1

```{r per Stenosis per Sex}

# Global test
compare_means(HMOX1 ~ StenoticGroup,  data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HMOX1", 
                  xlab = "Stenotic grade",
                  ylab = "HMOX1 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Stenosis.pdf"), plot = last_plot())

compare_means(HMOX1 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HMOX1", 
                  xlab = "Stenotic grade per gender",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```


## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AERNASE.clin$AgeGroup, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$Gender, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$AsymptSympt2G)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per symptom group as median ± interquartile range.

### HMOX1

```{r per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "HMOX1",
                  title = "HMOX1 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HMOX1 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

compare_means(HMOX1 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "HMOX1",
                  title = "HMOX1 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "HMOX1 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```


## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target

```

Forest plots.

### HMOX1

```{r forestplots}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="HMOX1"]),
                  low = c(models_target$low95CI[models_target$Predictor=="HMOX1"]),
                  high = c(models_target$up95CI[models_target$Predictor=="HMOX1"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque HMOX1 normalized expression (1 SD increment, n = 622)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1.plaque.forest.pdf"), plot = fp)

rm(fp)
```


## Target expression vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the `r TRAIT_OF_INTEREST` plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the `r TRAIT_OF_INTEREST`` plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data

```{r}
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"
names(AERNASE.clin)[names(AERNASE.clin) == "MMP9"] <- "MMP9rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin <- merge(AERNASE.clin, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

```


```{r Target vs Cytokines INRT, paged.print=TRUE}

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin <-  AERNASE.clin %>% mutate(AERNASE.clin[,var.temp] == replace(AERNASE.clin[,var.temp], AERNASE.clin[,var.temp]==0, NA))

  AERNASE.clin[,var.temp][AERNASE.clin[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin[,var.temp])),").\n"))
  
  AERNASE.clin <- AERNASE.clin %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin[,var.temp] - mean(AERNASE.clin[,var.temp], na.rm = TRUE))/sd(AERNASE.clin[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin[,var.temp.rank] <- NULL
  names(AERNASE.clin)[names(AERNASE.clin) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r Target vs Cytokines Histograms}
proteins_of_interest_rank_target <- c("HMOX1", proteins_of_interest_rank)

proteins_of_interest_target <- c("HMOX1", proteins_of_interest)

for(PROTEIN_GENE in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN_GENE in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `r TRAIT_OF_INTEREST` and 28 other cytokines. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r Target vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages.auto("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("HMOX1 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r Target vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of `r TRAIT_OF_INTEREST`` with other cytokines only.

### HMOX1 

```{r Target vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HMOX1 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold

p1 <- ggpubr::ggbarplot(temp, 
                x = "CytokineY", 
                y = "SpearmanRho",
                fill = "CytokineY",               # change fill color by cyl
                # color = "white",            # Set bar border colors to white
                # palette = uithof_color,            # jco journal color palett. see ?ggpar
                xlab = "Cytokine",
                sort.val = "desc",          # Sort the value in dscending order
                sort.by.groups = FALSE,     # Don't sort inside each group
                x.text.angle = 45, # Rotate vertically x axis texts
                cex = 1.25
                )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(paste("Spearman's"~italic(rho))))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)


```

Another version - probably not good. 
```{r}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HMOX1 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           # palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           # ylab = expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 4, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(log[10]~"("~italic(p)~")-value"))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HMOX1_vs_Cytokines.dotchart.pdf"), plot = last_plot())

rm(temp, p1)

```


## Target expression vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL1 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## Target expression vs. vulnerability index


Here we plot the levels of inverse-rank normal transformed `r TRAIT_OF_INTEREST` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 

```{r Fix ORyearGroup, message=FALSE, warning=FALSE}
library(sjlabelled)

AERNASE.clin$yeartemp <- as.numeric(year(AERNASE.clin$dateok))

attach(AERNASE.clin)

AERNASE.clin[,"ORyearGroup"] <- NA
AERNASE.clin$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin)

table(AERNASE.clin$ORyearGroup, AERNASE.clin$ORdate_year)
```

### Visualisations

#### HMOX1

```{r per PlaqueVulnerabilityIndex}
# Global test

compare_means(HMOX1 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HMOX1", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HMOX1 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".HMOX1.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HMOX1", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "HMOX1 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".HMOX1.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())




compare_means(HMOX1 ~ Plaque_Vulnerability_Index, data = AERNASE.clin, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HMOX1", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HMOX1 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".HMOX1.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

compare_means(HMOX1 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HMOX1", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HMOX1 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".HMOX1.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())

```


### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability index as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, paged.print=TRUE}
TRAITS.TARGET.RANK.extra = c("HMOX1")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, paged.print=TRUE}

for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.1.1
    Last update:  2024-01-09
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse Targets from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.1.1 Textual fixes.
    * v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
    * v1.0.1 Fix to the start of this notebook.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2024 Sander W. van der Laan | s.w.vanderlaan[at]gmail[dot]com | [vanderlaanand.science](https://vanderlaanand.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+
